Semantic Indexing for Music Search with Adaptive Recommendation

Due to the rapidly increasing availability of digital music, advanced semantic music search and music recommendation are becoming important issues. Thus how to index music for semantic search and how to make recommendations in intrinsic of musical art are challenging problems currently. In this context, this paper used a semantic indexing method that is capable of flexibly retrieval music in semantic level. When the semantic and dynamic index has been established, updating, increasing and decreasing index scores are necessary to build a reinforcement index to gain accurately search results. Moreover, the paper also proposed useful and flexible music recommendation approaches, for example song-to-song matching and multi-song matching methods. In order to optimize the order of search result, genetic algorithm also used to reranking those near hidden music in the top. The proposed method will also support users with diverse needs when searching for music. The observations indicate that the present approach is able to get better performance.